Impact of interactions between layers on source localization in multilayer networks

Nowadays it is not uncommon to have to deal with dissemination on multilayer networks and often finding the source of said propagation can be a crucial task. We examine the issue of locating the source of Susceptible–Infected spreading process in a multilayer network using the Bayesian inference and the maximum likelihood method established for general networks and adapted here to cover multilayer topology. We show how its accuracy depends on network and spreading parameters and find the existence of two parameter ranges with different behavior. If inter-network spreading rate is low, observations in different layers interfere, lowering accuracy below that of relying on single layer observers only. If it is high, on the other hand, observations synergize, raising accuracy above the level of single layer network of the same size and observer density. We also show a heuristic method to determine the case in a system and potentially improve accuracy by rejecting interfering observations. This paper is dedicated to the memory of Professor Dietrich Stauffer, who was a pioneer in new approaches in statistical physics and its interdisciplinary applications.